
WMMSE [1]} forms the baseline for our experiments as it is the most commonly followed classical approach to address this problem. We set a maximum of 100 iterations for WMMSE per sample.

Truncated WMMSE (Tr-WMMSE) provides a performance lower bound to UWMMSE in terms of the sum-capacity that WMMSE achieves with as many iterations as the unrolled layers without the learning component. We fix the number of iterations of Tr-WMMSE to 4 to match UWMMSE unrollings.

MLP [2]} is employed in a supervised setting to learn a functional mapping between channel state information and corresponding power allocation, using WMMSE output as the training signal.

REGNN [3]} addresses the specific problem of binary power allocation in Rayleigh interference channels. It tackles this problem in the high-noise regime where it achieves state-of-the-art performance.

IGCNet [4]} addresses the power allocation problem in Gaussian interference channels, specifically, in the high-noise regime, where it achieves state-of-the-art performance.

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